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How to Handle the 3 Most Time-Consuming Data Management Activities

Here are effective strategies for addressing the three most time-intensive data management tasks.

6 Min Read
blocks of data

With the exponential growth of data, organizations are faced with managing vast amounts of data in various formats. Data operations professionals are challenged with meeting the business needs for an entire organization, which places substantial demands on them, and on the data. Good data management practices ensure the data is clean, synchronized, and available. But this is easier said than done and the operational burden and runaway data costs take their toll on data ops professionals.

Here we review the three most time-intensive data management tasks, explore their significance, and discuss effective strategies to address them.

Time Consumer No. 1: Data Discovery

When data needs to be utilized by a specific application, team, or service, the data is typically replicated and migrated to the necessary location. This can lead to data being stored in different locations, systems, or formats — ultimately resulting in data silos. These silos can hinder data users' ability to discover and access data across the organization. Data silos fragment the data, which affects data quality and can lead to data inconsistencies. Fragmented data hinders an organization's ability to effectively leverage, or even trust, their data.

Data governance is crucial for data quality, management, and stewardship. It not only defines access controls but data usage policies as well. Lack of governance can lead to data becoming scattered across various storage locations or databases, making it difficult to locate and consolidate. When asked to identify challenges encountered by their data producers, 42% of organizations said real-time distribution of synchronized data, according to Enterprise Strategy Group's July 2023 survey, The State of DataOps.

Related:10 Tips for Developing a Data Governance Strategy

In the context of data replication or migration, data integrity can be compromised, resulting in inconsistencies or discrepancies between the source and target systems. This issue is identified as the second most common challenge faced by data producers, identified by 40% of organizations, according to The State of DataOps report. Replication processes generate redundant copies of data, while migration efforts may inadvertently leave extraneous data in the source system. Consequently, this situation can lead to uncertainty regarding which data version to rely upon and can result in wasteful consumption of storage resources. Large-scale data replication introduces a substantial volume of data, thereby increasing the time and effort required to locate specific information.

Many datasets exhibit intricate hierarchies and relationships among various data components. Regrettably, replication or migration processes may not consistently preserve these complex associations, thereby impeding users' ability to access data within its appropriate context. Moreover, security measures and access controls can, and do, change, potentially leading to unauthorized access or difficulties in locating data due to modified permissions. In some instances, these changes can render critical data inaccessible or be permanently lost.

Time Consumer No. 2: Data Availability

Efficient and secure data access for authorized users is vital for data-driven decision-making, innovation, and efficiency. It enables organizations to harness the full potential of their data assets while ensuring data security and compliance. As organizations amass large volumes of data, it becomes increasingly difficult to move or replicate, particularly when it resides in on-premises data centers or cloud storage. When data is concentrated in one location, accessing it from remote locations can result in latency and performance issues, particularly relevant for global organizations with distributed teams. Data tends to accumulate in one place, making it challenging to move or access efficiently, especially as it grows. This phenomenon is known as data gravity. Data gravity can also lead to the creation of data silos as different departments or teams accumulate data independently, hindering collaboration and holistic data analysis.

Another factor affecting data availability is the use of multiple cloud service providers and software vendors. Each offers proprietary tools and services for data storage and processing. Organizations that heavily invest in one platform may find it challenging to switch to an alternative due to compatibility issues. Transitioning away from an ecosystem can incur substantial costs and effort for data migration, application reconfiguration, and staff retraining. Ultimately, vendor lock-in can restrict an organization's flexibility to choose the best-fit solutions for evolving needs, hindering innovation and responsiveness to technology trends.

Ensuring data portability between different vendor platforms is the ideal answer, but too complex to manually manage. To address these challenges, organizations often adopt strategies like multi-cloud architectures to strike a balance between leveraging the benefits of specific vendors or platforms and ensuring data remains accessible, flexible, and portable. Even as new applications and AI platforms proliferate, data access solutions that span multiple clouds help foster organizational collaboration and informed decision-making.

Time Consumer No. 3: Metadata Management

Data trust is a top concern for any organization looking to leverage their data for business insights and decision-making. For example, 36% of organizations said their data users face inconsistencies in data across different systems and sources, representing one of the most common challenges among these personnel, according to The State of DataOps report. Meanwhile, the report also found that 62% of line-of-business stakeholders only somewhat trust their organizations' data. This can be solved, in part, through better metadata management.

Metadata provides important context and details about various aspects of data, including its structure, content, source, ownership, and usage. A major problem is that metadata can change during replication or migration and differences in metadata between the source and target systems can make it challenging to search for and identify the right data. Metadata management ensures that metadata is accurate, consistent, and readily accessible to support efficient data management and decision-making. It also empowers business users to efficiently search for and locate essential information related to key attributes. Poor metadata management can lead to difficulties in effectively organizing and categorizing data attributes, resulting in data inconsistencies and reduced data accessibility.

Metadata management is particularly time-consuming due to the ever-expanding complexity and volume of metadata and the challenges associated with harmonizing metadata from diverse data sources. It requires ongoing updates in the face of constant data changes, and maintaining metadata quality for the purposes of compliance is critical. Implementing a comprehensive metadata management system ensures data consistency, facilitates data discovery, and streamlines data governance processes. By ensuring that metadata is accurately documented, easily accessible, and consistently applied, organizations can better leverage their data assets while maintaining data quality and compliance.

A comprehensive metadata management system enforces standardized meta data practices, including naming conventions, data descriptions, and attribute classifications, to ensure that data elements are uniformly described and categorized. They map data relationships and lineage to maintain data consistency and provide search and cataloging capabilities, making it easier for users to discover relevant data assets via keywords, attributes, classifications, or other metadata criteria. Data lineage, classification, and searchability also streamline data governance and compliance, providing an audit trail, standards, and documentation.

In all, efficient data management is time-consuming but a fundamental aspect of modern decision-making. Multi-cloud architectures can help strike the balance between leveraging the benefits of specific vendors or platforms and ensuring data remains accessible, flexible, and portable. The best multi-cloud providers deliver a cohesive data access framework and good metadata management features that promote data integration, help organizations maintain data accuracy and compliance, and empower business users to get their hands on the high-quality data they need to create a competitive edge.

Kate Sandoval is senior product marketing manager at Faction, Inc.

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